Autonomous driving systems face the formidable challenge of navigating intricate and dynamic environments with uncertainty. This study presents a unified prediction and planning framework that concurrently models short-term aleatoric uncertainty (SAU), long-term aleatoric uncertainty (LAU), and epistemic uncertainty (EU) to predict and establish a robust foundation for planning in dynamic contexts. The framework uses Gaussian mixture models and deep ensemble methods, to concurrently capture and assess SAU, LAU, and EU, where traditional methods do not integrate these uncertainties simultaneously. Additionally, uncertainty-aware planning is introduced, considering various uncertainties. The study's contributions include comparisons of uncertainty estimations, risk modeling, and planning methods in comparison to existing approaches. The proposed methods were rigorously evaluated using the CommonRoad benchmark and settings with limited perception. These experiments illuminated the advantages and roles of different uncertainty factors in autonomous driving processes. In addition, comparative assessments of various uncertainty modeling strategies underscore the benefits of modeling multiple types of uncertainties, thus enhancing planning accuracy and reliability. The proposed framework facilitates the development of methods for UAP and surpasses existing uncertainty-aware risk models, particularly when considering diverse traffic scenarios. Project page: https://swb19.github.io/UAP/.

翻译：自动驾驶系统面临在复杂动态环境中导航的不确定性挑战。本研究提出一种统一的预测与规划框架，该框架同时建模短期偶然不确定性（SAU）、长期偶然不确定性（LAU）和认知不确定性（EU），以预测并为动态环境下的规划建立稳健基础。该框架采用高斯混合模型与深度集成方法，同时捕获并评估SAU、LAU和EU，而传统方法无法同时整合这些不确定性。此外，本研究引入考虑多种不确定性的不确定性感知规划。研究贡献包括对不确定性估计、风险建模及规划方法与现有方法的比较。所提方法利用CommonRoad基准测试和有限感知设置进行了严格评估，实验揭示了不同不确定性因素在自动驾驶过程中的优势与作用。同时，对多种不确定性建模策略的比较评估强调了建模多种不确定性类型的益处，从而提升了规划的准确性与可靠性。所提框架促进了不确定性感知规划方法的发展，并在多种交通场景下超越了现有不确定性感知风险模型。项目页面：https://swb19.github.io/UAP/。